A hands-on introduction to machine learning:
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Cambridge
Cambridge University Press
2023
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| Schlagwörter: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034069485&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Umfang: | xxii, 412 Seiten Illustrationen, Diagramme |
| ISBN: | 9781009123303 |
Internformat
MARC
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Datensatz im Suchindex
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| adam_text | Contents 1 Preface Acknowledgments About the Author page xiii xix xxi Parti Basic Concepts 1 Teaching Computers to Write Programs 1.1 What Is Machine Learning? 1.2 Branches of Machine Learning 1.3 What Do You Need for Machine Learning? 1.3.1 Skills 1.3.2 Tools 1.4 Computational Thinking 1.5 Issues of Ethics, Bias, and Privacy in Machine Learning 1.6 Summary Key Terms Conceptual Questions Hands-on Problems . Further Reading and Resources 2 Python 2.1 Introduction 2.2 Getting Access to Python 2.2.1 Download and Install Python 2.2.2 Running Python through the Console 2.2.3 Using Python through an Integrated Development Environment 2.3 Basic Examples 2.4 Control Structures 2.5 Functions 2.6 Making Python Interactive 2.7 Installing and Using Python Packages 2.7.1 Spyder 2.7.2 Jupyter Notebook 2.7.3 Google Colab vii 3 3 9 11 11 13 15 18 19 20 20 21 25 26 26 26 26 27 27 29 32 34 36 36 37 37 37
2.8 Statistics Essentials 2.8.1 Importing Data 2.8.2 Plotting the Data 2.8.3 Correlation 2.9 Summary Key Terms Conceptual Questions Hands-on Problems Further Reading and Resources 3 Cloud Computing 3.1 Cloud Computing 3.2 Google Cloud Platform 3.2.1 Hadoop 3.3 Microsoft Azure 3.4 Amazon Web Services 3.5 Moving between Cloud Platforms 3.6 Summary Key Terms Conceptual Questions Hands-on Problems Further Reading and Resources 39 42 43 44 45 45 46 46 48 49 49 50 53 59 66 75 75 76 76 76 77 Part II Supervised Learning 4 Regression 4.1 Introduction 4.2 Linear Regression 4.3 Multiple Linear Regression 4.4 Ridge and Lasso Regression 4.5 Gradient Descent 4.6 Considerations for ML Modeling 4.6.1 Accuracy 4.6.2 Training-Testing 4.6.3 Training Time 4.6.4 Linearity 4.6.5 Number of Hyperparameters 4.6.6 Number of Features 4.6.7 Choosing the Right Estimator 4.7 Summary Key Terms Conceptual Questions Hands-on Problems Further Reading and Resources 81 81 82 91 96 98 110 111 111 111 111 112 112 112 113 113 114 114 117
Contents Classification 1 5.1 Introduction 5.2 Classification with ¿-Nearest Neighbors 5.3 Decision Tree 5.3.1 Decision Rule 5.3.2 Classification Rule 5.3.3 Association Rule 5.4 Random Forest 5.5 Summary Key Terms Conceptual Questions Hands-on Problems Further Reading and Resources Classification 2 6.1 Introduction 6.2 Logistic Regression 6.3 Softmax Regression 6.4 Naïve Bayes 6.5 Support Vector Machine 6.6 Summary Key Terms Conceptual Questions Hands-on Problems Further Reading and Resources 118 118 119 123 127 127 128 132 137 137 138 138 143 144 144 145 152 154 159 165 166 166 167 170 Part III Unsupervised Learning Clustering 7.1 Introduction 7.2 Divisive Clustering 7.2.1 Divisive Clustering with ¿-Means 7.2.2 Divisive Clustering with ¿-Modes 7.3 Agglomerative Clustering 7.4 Expectation Maximization 7.5 Density Estimation 7.6 Summary Key Terms Conceptual Questions Hands-on Problems Further Reading and Resources 175 175 176 179 182 186 192 196 199 200 200 200 204
Contents Dimensionality Reduction 8.1 Introduction 8.2 Feature Selection 8.3 Principal Component Analysis 8.4 Linear Discriminant Analysis 8.5 Summary Key Terms Conceptual Questions Hands-on Problems Further Reading and Resources 206 206 207 214 218 221 222 222 222 223 Part IV Neural Networks Neural Networks 9.1 Mimicking the Human Brain 9.1.1 Basics of a Neural Network 9.1.2 Human vs. Artificial Neuron 9.2 Architectures of Basic Neural Networks 9.2.1 Feed-Forward Network 9.2.2 Perceptrons 9.2.3 Single-Layer Perceptron 9.2.4 Multilayer Perceptron 9.3 Autoencoders 9.4 Convolution Neural Network 9.5 Recurrent Neural Network 9.6 Long Short-Term Memory 9.7 Summary Key Terms Conceptual Questions Hands-on Problems Further Reading and Resources Deep Learning 10.1 Introduction 10.2 When and How to Use a Deep Model 10.3 Building Your First Deep Learning Model with Python 10.4 Building a Classifier with Deep Learning 10.5 Using Deep Models for Embeddings 10.6 Encoders and Transformers 10.6.1 Input Embeddings 10.6.2 Positional Encoding 10.6.3 Encoder Layer 10.6.4 Multi-headed Attention 10.7 Summary 227 227 228 228 229 231 231 232 235 243 247 251 256 259 260 260 261 263 264 264 265 266 272 278 283 285 285 286 286 297
Contents xi Key Terms Conceptual Questions Hands-on Problems Further Reading and Resources 298 298 299 300 Part V Further Explorations 11 Reinforcement Learning 11.1 Introduction 11.2 Conceptualizing RL 11.3 Doing RL by Hand 11.4 2֊ learning 11.5 Bandits 11.5.1 Multi-arm Bandits 11.5.2 Contextual Bandits 11.6 Summary Key Terms Conceptual Questions Hands-on Problems Further Reading and Resources 303 303 304 306 311 315 316 316 322 323 323 323 324 12 Designing and Evaluating ML Systems 12.1 Introduction 12.2 Thinking through an ML Solution 326 326 327 331 331 335 337 340 344 344 345 345 346 347 Evaluation 12.3.1 Offline Evaluation 12.3.2 User Research 12.3.3 A/В Testing 12.3.4 Counterfactual Evaluation 12.3.5 Adversarial Learning 12.4 Summary Key Terms Conceptual Questions Hands-on Problems Further Reading and Resources 12.3 13 Responsible Al 13.1 13.2 13.3 13.4 Introduction Diversity vs. Misinformation: A Case Study Responsible AI in Academia, Industry, and Regulations Bias and Fairness 13.4.1 Where Does Bias Come From? 13.4.2 Bias Is Everywhere 13.4.3 Bias vs. Fairness 348 348 349 352 353 353 354 355
Contents xii 13.5 Accountability and Auditing 13.6 Transparency and Explainability 13.7 Summary Key Terms Conceptual Questions Hands-on Problems Further Reading and Resources Appendices Index 356 358 361 363 363 363 364 367 405
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| indexdate | 2024-12-20T19:52:19Z |
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| isbn | 9781009123303 |
| language | English |
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| spellingShingle | Shah, Chirag 1978- A hands-on introduction to machine learning Maschinelles Lernen (DE-588)4193754-5 gnd Cloud Computing (DE-588)7623494-0 gnd Soziale Verantwortung (DE-588)4055737-6 gnd |
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| title | A hands-on introduction to machine learning |
| title_auth | A hands-on introduction to machine learning |
| title_exact_search | A hands-on introduction to machine learning |
| title_full | A hands-on introduction to machine learning Chirag Shah, University of Washington |
| title_fullStr | A hands-on introduction to machine learning Chirag Shah, University of Washington |
| title_full_unstemmed | A hands-on introduction to machine learning Chirag Shah, University of Washington |
| title_short | A hands-on introduction to machine learning |
| title_sort | a hands on introduction to machine learning |
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| topic_facet | Maschinelles Lernen Cloud Computing Soziale Verantwortung Lehrbuch |
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